Hunting for Polluted White Dwarfs and Other Treasures with Gaia XP Spectra and Unsupervised Machine Learning

TL;DR

This study uses Gaia XP spectra and unsupervised machine learning to identify and increase the detection of polluted white dwarfs with multiple metals, aiding exoplanetary research.

Contribution

Introduces a novel spectral mapping method with UMAP to find polluted white dwarfs, significantly expanding known populations for exoplanet studies.

Findings

Polluted WDs form distinct spectral groups in the UMAP map.

Method could increase known WDs with multiple metals by an order of magnitude.

Enhanced detection of exoplanetary material in white dwarfs.

Abstract

White dwarfs (WDs) polluted by exoplanetary material provide the unprecedented opportunity to directly observe the interiors of exoplanets. However, spectroscopic surveys are often limited by brightness constraints, and WDs tend to be very faint, making detections of large populations of polluted WDs difficult. In this paper, we aim to increase considerably the number of WDs with multiple metals in their atmospheres. Using 96,134 WDs with Gaia DR3 BP/RP (XP) spectra, we constructed a 2D map using an unsupervised machine learning technique called Uniform Manifold Approximation and Projection (UMAP) to organize the WDs into identifiable spectral regions. The polluted WDs are among the distinct spectral groups identified in our map. We have shown that this selection method could potentially increase the number of known WDs with 5 or more metal species in their atmospheres by an order of magnitude. Such systems are essential for characterizing exoplanet diversity and geology.